A new analysis of gravitational wave (and other data) from GW170817 on 2017-Aug-17 has been published, strongly suggesting that the merger of two neutron stars resulted in a large, rapidly rotating neutron star, rather than a black hole. From the abstract of the recent open access Letter in MNRASObservational evidence for extended emission to GW170817:

[...]Here, we report on a possible detection of extended emission (EE) in gravitational radiation during GRB170817A: a descending chirp with characteristic time-scale τs = 3.01 ± 0.2 s in a (H1,L1)-spectrogram up to 700 Hz with Gaussian equivalent level of confidence greater than 3.3σ based on causality alone following edge detection applied to (H1,L1)-spectrograms merged by frequency coincidences. Additional confidence derives from the strength of this EE. The observed frequencies below 1 kHz indicate a hypermassive magnetar rather than a black hole, spinning down by magnetic winds and interactions with dynamical mass ejecta.

I am having trouble understanding what GPU-accelerated butterfly filtering of H1, L1 and V1 by matched filtering over a dense bank of time-symmetric chirp-like templates means. van Putten et al. 2014 in the supplementary data document refers to both BROADBAND TURBULENT SPECTRA IN GAMMA-RAY BURST LIGHT CURVES and to as well. These appear to be thorough explanations, but pretty in-depth.

Question: Is it possible to explain the basics of what "GPU-accelerated butterfly filtering of H1, L1 and V1 by matched filtering over a dense bank of time-symmetric chirp-like templates" means? A dense bank of chirp-like templates sounds like it could be analogous to a wavelet-type analysis, but with basis waveforms tailored to this specific problem.

First image is a cropped and annotated version of the second which ia from here.

$\begingroup$GPU-accelerated butterfly filtering is a computer-program filtering algorithm, to filter only signals out of the data. time-symmetric chirp-like templates are templates of signals of various types (both GW and non GW). The detection algorithm of the GW is that, the system has a library (i.e., a dense bank) of various known types of signals, and how they would look like. From that, once you have data you can compute detection probability like false-alarm one.$\endgroup$
– Kornpob BhirombhakdiNov 16 '18 at 13:48

$\begingroup$@KornpobBhirombhakdi thanks! That's an excellent functional summary and I appreciate it. I am hoping that when an answer is posted, it will explain a bit more of the mathematical aspects. For example, I certainly understand that "chirp-like templates are templates of signals of various types" but that's a bit circular. I'm also comfortable with using a library search (rather than an optimization using a basis set) and so that is very helpful. I wonder what one template might "look like". Would it be simply a 1 millisecond long strain waveform?$\endgroup$
– uhohNov 16 '18 at 15:03

$\begingroup$@KornpobBhirombhakdi if you know of (or can find) a reference that describes how the library is built, that might help towards finding an answer. I can read it and then post an answer myself if nobody else is interested. Thanks!$\endgroup$
– uhohDec 5 '18 at 8:41

$\begingroup$@KornpobBhirombhakdi I did talk about the results of Abbot 2017 here but I will go back now and have a look in section 2 (and 3) and references therein, thank you!$\endgroup$
– uhohDec 5 '18 at 16:53